The present disclosure relates generally to using probabilistic event networks to predict future events, and more specifically, to mode determination for multivariate time series data.
Increasing digitization of enterprise internal operations as well as external environments implies an availability of information about a large amount of ordinary events that occur within and around an enterprise. For example, systems for enterprise resource planning (ERP), supply chain management (SCM), or customer relationship management (CRM) record many of the events related to the corresponding management areas. Also, various types of sensors provide information about events related to physical assets. Given a stream of primitive data about ordinary events, actionable information may be extracted to allow reasoning and decision-making in real-time. Such sensor data often is in the form of one or more multivariate time series. Multivariate time series are typically generated from similar types of sensor networks, for example, multiple pieces of similar types of equipment, each instrumented with the same type and set of sensors.